Hitherto, a transform encoding system using a so called spectrum transform, as a type of efficient encoding systems for efficiently bit-compressing a time series sample data signal such as an audio signal, etc., to encode bit-compressed signal, is known. This transform encoding system spectrum transforms an input signal in block units to encode transformed spectrum signals. Discrete Cosine Transform (DCT) processing is the representative spectrum transform processing. In this transform encoding system, a block distortion such that a discontinuous adjoining (connecting) portion between the blocks is felt as problematic noise. In order to lessen such block distortion, the end portions of such blocks are generally allowed to overlap with adjacent blocks. Here, a so called Modified Discrete Cosine Transform (MDCT), or Improved Discrete Cosine Transform (IMDCT) which is the inverse transform processing thereof, is performed such that double transmission with respect to samples at the overlapping portion is not carried out while allowing an arbitrary block to overlap with blocks adjoining in both directions thereof respectively by halves (half blocks), and is therefore suitable for efficient encoding.
Encoding and Decoding using such MDCT and IMDCT is disclosed in, e.g., Mochizuki, Yano, Nishitani "Filter Constraints of Plural Block Size Mixed MDCT", Technical Research Report of Institute of Electronics and Communication Engineers of Japan, CAS 90-10, DSP 90-14, pp. 55-60, or Hazu, Sugiyama, Iwatare, Nishitani "Adaptive block length Adaptive Transform Coding using MDCT (ATC-ABS)" Society of Electronics and Information Communication Engineers of Japan Spring National Meeting Lecture Collection of 1990, A-197. etc. The MDCT and the IMDCT mentioned above will be briefly described below with reference to FIG. 7.
In FIG. 7, an arbitrary block of time series sample data, e.g., the J-th block, has portions overlapping with the (J-1)-th block and the (J+1)-th block respectively by halves (50%). When the number of samples of the J-th block is assumed to be N (N is natural number), the J-th block has overlap of N/2 samples between the J-th block and the (J-1)-th block, and also has overlap of N/2 samples between the J-th block and the (J+1)-th block. Pre-processing filter or window Wh for transform is applied to these respective blocks, e.g., an arbitrary J-th block input time series sample 101 to obtain N (number of) time series data 102.
As with the characteristic of the pre-processing filter or the window Wh for transform, a characteristic where the degree of power concentration of transform data is maximum is selected in correspondence with the statistical property of an input signal. By implementing linear transform processing of MDCT to the time series data 102 of N samples, N/2 (i.e., one half of the number of input samples) independent spectrum data 103 are obtained on a frequency base. By implementing processing of linear inverse transform of IMDCT to the N/2 spectrum data 103, N time series data 104 is obtained. Synthesis filter or window Wf for inverse transform is applied to the time series data 104 to obtain time series data 105 thereafter to add it to output results of blocks before and after to restore (reconstruct) original input time series sample data.
In the conventional efficient encoding system, a method of re-quantizing spectrum data 103 obtained as described above by taking into the characteristic from a view point of the hearing sense to record or transmit re-quantized data has been adopted. In addition, as in the ISO 11172-3 of the ISO standard, entropy encoding such that short and long codes are respectively allocated to data of higher frequency and data of lower frequency in dependency upon occurrence frequency is implemented to all or a portion of these spectrum data. Thus, high efficiency is achieved.
However, in the case where entropy encoding is implemented in this way, the number of bits required for every respective block of time series sample data is variable, and the upper limit of the number of bits thereof cannot be recognized until an input signal is actually encoded. For this reason, not only are encoding and decoding at a fixed bit rate difficult, but also, the scale of hardware is large.